opening quote after a modal verb
A neuronOne of thousands of tiny dials inside each layer. It reads the text so far and produces a number, its activation, that nudges what the model writes next. in layerOne step in the stack the text passes through. Qwen3-0.6B has 28 layers, each with 3,072 neurons. 13 of Qwen3-0.6B. Its strongest snippets are very consistent: they mostly peak on the same token. It is featuredA neuron picked for the full treatment: steering outputs and a written explanation. About 3,000 of the 86,000 neurons are featured., so it has steering outputs.
What this neuron does
This neuron lights up on an opening quotation mark that comes right after words like "can", "could" or "may", where a word or phrase is being put in quotes for effect. When it was turned up, the model's writing drifted into rambling and unrelated topics, so the quote pattern is the only clear link.
Written by Claude from this neuron's activations and steering outputs. Confidence: medium. Think it is wrong? Flag it (opens a prefilled issue on GitHub).
Fires most on
The text windows where this neuron's activationThe number a neuron produces for a token. Large positive means it fired hard; negative means it pushed the other way. was highest. Amber marks positive activation; the outlined tokenA piece of text the model reads at once: often a word, sometimes part of a word or a punctuation mark. is the peak. Hover a token for its strength.
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Their study, which is published in the journal Proceedings of the National Academy of Sciences (PNAS), is the first to identify a non-primate species as having “orthographic” abilities.⏎In the experiment, pigeons were trained to peck
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.⏎The combined effect was that Myotragus was sluggish, with slow reaction times, the bone study suggests.⏎Like modern-day reptiles, the goats probably “saved as much energy as possible just lying around and basking in the sun,”
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to 100⏎tspan=[t0 tend]; %need to use it in this form in MATLAB 5.⏎[t,y]=ode45('rhs',tspan,y0)%call the routine ode45⏎plot(t
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BUQUERQUE, NM – A team of researchers from Sandia, Lawrence Berkeley and Pacific Northwest National Laboratories tested a “sponge-like” mineral that can “absorb” uranium at a former uranium
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Health Checklist⏎Being a college student warrants a quick health check in order for you to remain healthy all school year. By following a few simple strategies, you can “rock on” in school, work and your social life.⏎Get involved in regular
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landing, as well as what it is like to do a spacewalk<|endoftext|>Global warming “can be reversed", scientists claim⏎Hi-tech new bio-energy plants could “reverse” global warming by pumping carbon dioxide into old gas wells - lowering temperatures by
SteeringAdding a push along a neuron while the model writes, to see how its output changes. Turning it up or down.
Push this neuron up or down while the model writes, and watch what changes. The outputs were generated ahead of time, so nothing runs in your browser.
Here is something I have been thinking about lately: " ' ' " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " "
Most negative on
Where the activation went furthest below zero. Blue marks negative activation. Many neurons mean something different in each direction.
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', 'pomegranite', 'bananana', 'grape', 'loganberry'] print "You are an" , random.choice (verb) , random.choice (fruit)⏎Any Tutorial Videos?<|endoftext|>What is globalisation?⏎Global
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Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device (ALDM) Test Systems⏎Institutions: Aix-Marseille University.⏎Fag
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that will give a useful input to those interested in Artificial Intelligence in education.⏎|Subjects:||L Education > L Education (General)|⏎|Deposited By:||Zaidatun Tasir|⏎|Deposited On:||
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structure = function.⏎(Image from: https://commons.wikimedia.org/w/index.php?cur
Connections
Show on mapWhich neuronsOne of thousands of tiny dials inside each layer. It reads the text so far and produces a number, its activation, that nudges what the model writes next. in earlier layersOne step in the stack the text passes through. Qwen3-0.6B has 28 layers, each with 3,072 neurons. feed this one, and which later ones it feeds. Amber links excite, blue links suppress; longer bars are stronger.
Fed by earlier layers
- L8:257 suppresses
- L5:2501 weakly excites
- L11:769 weakly excites
- L3:500 weakly suppresses
- L0:2845 weakly excites
- L11:226 weakly suppresses
- L9:49 weakly suppresses
- L1:100 weakly excites
- L3:2558 weakly excites
- L0:2355 weakly suppresses
- L12:80 weakly suppresses
- L8:543 weakly excites "Since" starting a reason clause
- L2:2634 weakly suppresses
- L9:2728 weakly excites
- L4:1 weakly excites
- L7:770 weakly excites
- L12:1513 weakly excites
- L4:919 weakly excites
- L12:3039 weakly excites
- L12:549 weakly suppresses
- L1:1907 weakly suppresses
- L6:2173 weakly excites
- L8:423 weakly excites
- L11:2160 weakly suppresses
Feeds later layers
- L17:893 excites
- L17:811 suppresses
- L14:1111 excites
- L14:313 suppresses
- L18:2897 excites
- L16:2206 suppresses
- L17:2616 excites
- L15:1515 suppresses
- L18:1945 excites
- L18:156 excites
- L20:872 excites
- L18:2179 excites
- L19:1616 excites
- L18:2361 excites
- L24:286 suppresses
- L20:1677 excites
- L18:606 excites
- L15:1381 excites
- L16:2067 weakly excites
- L14:1530 weakly excites
- L16:211 weakly suppresses
- L19:1409 weakly suppresses
- L16:1633 weakly excites
- L16:354 weakly suppresses
Activation histogramA chart of how often a neuron takes each activation value.
How often this neuron fires at each strength. Almost always it does close to nothing (the tall bars in the middle); the snippets above are the rare moments it fired hard, amber for positive and blue for negative.
Logit lensReading which output tokens a neuron pushes toward or away from, straight off its output weights, before the later layers have their say.
The words this neuron nudges the model toward (amber) or away from (blue) when it fires. A hint, not a label: many neurons work through later layersOne step in the stack the text passes through. Qwen3-0.6B has 28 layers, each with 3,072 neurons. instead.
Promotes
- preter
- ame
- odor
- imedia
- дачи
- бел
- enger
- apore
- 埗
- continuous
Suppresses
- !
- accord
- lav
- !⏎⏎
- Accord
- !)⏎⏎
- !)
- !⏎⏎
- function
- 犰